Is it theoretically possible (or impossible) that principal component analysis worsens the performance of the model?
PCA can make models worse,  imagine data points scattered along two elongated parallel rectangles. The axis with the greatest variation will be parallel to the rectangles but doesn't provide any benefit in classifying the points.